ASC TLR10 Proteomics - Differential Expression Analysis
Setup
source("helpers.R")
dir.create("results/DEA", showWarnings = F, recursive = T)
knitr::opts_chunk$set(fig.width = 10, dpi = 300, results = "hold", fig.show = "hold")# Heatmaps
hm_cluster_rows <- TRUE # Genes
hm_cluster_cols <- TRUE # Samples
hm_scale_by_row <- TRUE
hm_max_rows <- 20
heatmap.colors <- colorRamp2(c(-2, 0, 2), c("darkblue", "white", "darkred"))
# Volcano plot
vp_lfc_limit <- 5Undifferentiated
Supernatant
data <- read_DIA_report("data/MS1MS2/20240701_082307_ASC_SN_TLR10_Light_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name") plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df, full.groups = 1)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
meanSdPlot(assay(data.log2, "MS1"))
meanSdPlot(assay(data.log2, "MS2"))
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Loading required namespace: norm## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...5...6...
## Iterations of EM:
## 1...2...3...4...5...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "ASC_WT.Light - ASC_WT.Dark",
LightVsDarkInTLR10 = "ASC_TLR10_LOV.Light - ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "ASC_TLR10_LOV.Dark - ASC_WT.Dark",
TLR10VsWTInLight = "ASC_TLR10_LOV.Light - ASC_WT.Light",
Diff = "(ASC_TLR10_LOV.Light - ASC_TLR10_LOV.Dark) - (ASC_WT.Light - ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_WT_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_WT_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC Wildtype Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 189 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 262 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_vs_WT_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_vs_WT_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV vs. Wildtype Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 192 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_vs_WT_Light_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_vs_WT_Light_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV vs. Wildtype Light Supernatant", vp_lfc_limit)## Warning: ggrepel: 64 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC Interaction (TLR10LOV Light vs. Dark) vs. (Wildtype Light vs. Dark)
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_Interaction_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_Interaction_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows, cluster_rows = hm_cluster_rows,
cluster_cols = hm_cluster_cols, scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC Interaction Supernatant", vp_lfc_limit)
Whole Cell Lysate
data <- read_DIA_report("data/MS1MS2/20240701_082255_ASC_WCL_TLR10_Light_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
meanSdPlot(assay(data.log2, "MS1"))
meanSdPlot(assay(data.log2, "MS2"))
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...5...
## Iterations of EM:
## 1...2...3...4...5...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "ASC_WT.Light - ASC_WT.Dark",
LightVsDarkInTLR10 = "ASC_TLR10_LOV.Light - ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "ASC_TLR10_LOV.Dark - ASC_WT.Dark",
TLR10VsWTInLight = "ASC_TLR10_LOV.Light - ASC_WT.Light",
Diff = "(ASC_TLR10_LOV.Light - ASC_TLR10_LOV.Dark) - (ASC_WT.Light - ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/DEA/ASC_WT_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_WT_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)## [1] "Found 162 differentially expressed proteins."ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC Wildtype Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 121 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_vs_WT_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_vs_WT_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV vs. Wildtype Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 462 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_TLR10LOV_vs_WT_Light_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_TLR10LOV_vs_WT_Light_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC TLR10LOV vs. Wildtype Light Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 121 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ASC Interaction TLR10LOV Light vs. Dark vs. Wildtype Light vs. Dark
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ASC_Interaction_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ASC_Interaction_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_WT" = "lightblue",
"ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ASC Interaction Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 91 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC
Supernatant
data <- read_DIA_report("data/MS1MS2/20240701_082311_ADIPO_ASC_SN_TLR10_Light_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...5...6...
## Iterations of EM:
## 1...2...3...4...5...6...7...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "ADIPO_ASC_WT.Light - ADIPO_ASC_WT.Dark",
LightVsDarkInTLR10 = "ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "ADIPO_ASC_TLR10_LOV.Dark - ADIPO_ASC_WT.Dark",
TLR10VsWTInLight = "ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_WT.Light",
Diff = "(ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_TLR10_LOV.Dark) - (ADIPO_ASC_WT.Light - ADIPO_ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
ADIPO ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_WT_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_WT_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC Wildtype Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 130 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 467 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV vs. Wildtype Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 585 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Light_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Light_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV vs. Wildtype Light Supernatant", vp_lfc_limit)## Warning: ggrepel: 401 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC Interaction (TLR10LOV Light vs. Dark) vs. (Wildtype Light vs. Dark)
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_Interaction_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_Interaction_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows, cluster_rows = hm_cluster_rows,
cluster_cols = hm_cluster_cols, scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC Interaction Supernatant", vp_lfc_limit)
Whole Cell Lysate
data <- read_DIA_report("data/MS1MS2/20240626_081714_ADIPO_ASC_WCL_TLR10_Light_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...
## Iterations of EM:
## 1...2...3...4...5...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "ADIPO_ASC_WT.Light - ADIPO_ASC_WT.Dark",
LightVsDarkInTLR10 = "ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "ADIPO_ASC_TLR10_LOV.Dark - ADIPO_ASC_WT.Dark",
TLR10VsWTInLight = "ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_WT.Light",
Diff = "(ADIPO_ASC_TLR10_LOV.Light - ADIPO_ASC_TLR10_LOV.Dark) - (ADIPO_ASC_WT.Light - ADIPO_ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
ADIPO ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_WT_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_WT_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC Wildtype Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 47 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 66 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV vs. Wildtype Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 121 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Light_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_TLR10LOV_vs_WT_Light_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC TLR10LOV vs. Wildtype Light Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 90 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ADIPO ASC Interaction (TLR10LOV Light vs. Dark) vs. (Wildtype Light vs. Dark)
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/ADIPO_ASC_Interaction_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/ADIPO_ASC_Interaction_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ADIPO_ASC_WT" = "lightblue",
"ADIPO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "ADIPO ASC Interaction Whole Cell Lysate", vp_lfc_limit)
OSTEO ASC
Supernatant
data <- read_DIA_report("data/MS1MS2/20240701_082302_OSTEO_ASC_SN_TLR10_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...5...6...
## Iterations of EM:
## 1...2...3...4...5...6...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "OSTEO_ASC_WT.Light - OSTEO_ASC_WT.Dark",
LightVsDarkInTLR10 = "OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "OSTEO_ASC_TLR10_LOV.Dark - OSTEO_ASC_WT.Dark",
TLR10VsWTInLight = "OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_WT.Light",
Diff = "(OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_TLR10_LOV.Dark) - (OSTEO_ASC_WT.Light - OSTEO_ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
OSTEO ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_WT_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_WT_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC Wildtype Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 43 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV Light vs. Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Dark_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV vs. Wildtype Dark Supernatant", vp_lfc_limit)## Warning: ggrepel: 392 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Light_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Light_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV vs. Wildtype Light Supernatant", vp_lfc_limit)## Warning: ggrepel: 345 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC Interaction (TLR10LOV Light vs. Dark) vs. (Wildtype Light vs. Dark)
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_Interaction_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_Interaction_SN_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows, cluster_rows = hm_cluster_rows,
cluster_cols = hm_cluster_cols, scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC Interaction Supernatant", vp_lfc_limit)
Whole Cell Lysate
data <- read_DIA_report("data/MS1MS2/20240701_082250_OSTEO_ASC_WCL_TLR10_Report.tsv")
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.df, data_MS2.df)
keep <- filter_too_many_missing(data_MS1.df, data_MS2.df)
data.filtered <- data[rownames(data) %in% keep,]
data_MS1.filtered.df <- assay(data.filtered, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.filtered.df <- assay(data.filtered, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")plot_missing(data_MS1.filtered.df, data_MS2.filtered.df)
data.log2 <- data.filtered
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
data.norm <- data.filtered
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))
MNAR_MS1 <- get_MNAR(data_MS1.norm.df)
MNAR_MS2 <- get_MNAR(data_MS2.norm.df)
MNAR <- MNAR_MS1 | MNAR_MS2
plot_missing_heatmap(data_MS1.norm.df, data_MS2.norm.df, MNAR, colData(data.norm),
colors_columns = list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
colors_rows = list(MNAR=c("TRUE"="darkgreen",
"FALSE"="darkmagenta")))
plot_missing_density(data_MS1.norm.df, data_MS2.norm.df)
data.imputed <- data.norm
assays(data.imputed) %<>% lapply(MsCoreUtils::impute_mixed, !MNAR, "MLE", "MinDet")## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).
## Imputing along margin 1 (features/rows).data_MS1.imputed.df <- assay(data.imputed, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.imputed.df <- assay(data.imputed, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")## Iterations of EM:
## 1...2...3...4...5...
## Iterations of EM:
## 1...2...3...4...5...6...plot_pca(data.imputed, scale = T, plot_all = T, maxPC = 4)
contrast_list <- c(
LightVsDarkInWT = "OSTEO_ASC_WT.Light - OSTEO_ASC_WT.Dark",
LightVsDarkInTLR10 = "OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_TLR10_LOV.Dark",
TLR10VsWTInDark = "OSTEO_ASC_TLR10_LOV.Dark - OSTEO_ASC_WT.Dark",
TLR10VsWTInLight = "OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_WT.Light",
Diff = "(OSTEO_ASC_TLR10_LOV.Light - OSTEO_ASC_TLR10_LOV.Dark) - (OSTEO_ASC_WT.Light - OSTEO_ASC_WT.Dark)"
)
fit <- fit_DEqMS_model(data.imputed, contrast_list)
VarianceBoxplot(fit)
OSTEO ASC Wildtype Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "WT")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_WT_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_WT_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC Wildtype Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV Light vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "TLR10_LOV")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV Light vs. Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 66 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV vs. Wildtype Dark
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Dark_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV vs. Wildtype Dark Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 252 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC TLR10LOV vs. Wildtype Light
current_contrast <- 4
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene,
str_detect(colnames(data.imputed), "Light")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Light_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_TLR10LOV_vs_WT_Light_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC TLR10LOV vs. Wildtype Light Whole Cell Lysate", vp_lfc_limit)## Warning: ggrepel: 237 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
OSTEO ASC Interaction (TLR10LOV Light vs. Dark) vs. (Wildtype Light vs. Dark)
current_contrast <- 5
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.imputed[rownames(data.imputed) %in% res.sig$gene, ]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
write.csv(res, file = "results/DEA/OSTEO_ASC_Interaction_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/DEA/OSTEO_ASC_Interaction_WCL_filtered.csv",
row.names = FALSE)ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)
plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("OSTEO_ASC_WT" = "lightblue",
"OSTEO_ASC_TLR10_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)
plot_volcano(res, "OSTEO ASC Interaction Whole Cell Lysate", vp_lfc_limit)